Voluntary international coordination of programs for production of critical tables of standard reference data will be the aim of the committee on data for science and technology (codata), recently established by the i...
Voluntary international coordination of programs for production of critical tables of standard reference data will be the aim of the committee on data for science and technology (codata), recently established by the international council of scientific unions (icsu). Codata will have initial representation from 10 to 12 of the constituent unions of icsu and six major countries (france, germany, japan, u.k., u.s.a., and u.s.s.r.). A central staff office will be located initially in washington, d.c.
Generative AI (GenAI) offers transformative potential for exercise and sports science education, addressing traditional data analysis and visualization barriers while promoting real-world learning. This Perspectives a...
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Identifying vital nodes is one of the core issues of network science,and is crucial for epidemic prevention and control,network security maintenance,and biomedical research and *** this paper,a new vital nodes identif...
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Identifying vital nodes is one of the core issues of network science,and is crucial for epidemic prevention and control,network security maintenance,and biomedical research and *** this paper,a new vital nodes identification method,named degree and cycle ratio(DC),is proposed by integrating degree centrality(weightα)and cycle ratio(weight 1-α).The results show that the dynamic observations and weightαare nonlinear and non-monotonicity(i.e.,there exists an optimal valueα^(*)forα),and that DC performs better than a single index in most *** to the value ofα^(*),networks are classified into degree-dominant networks(α^(*)>0.5)and cycle-dominant networks(α^(*)<0.5).Specifically,in most degree-dominant networks(such as Chengdu-BUS,Chongqing-BUS and Beijing-BUS),degree is dominant in the identification of vital nodes,but the identification effect can be improved by adding cycle structure information to the *** most cycle-dominant networks(such as Email,Wiki and Hamsterster),the cycle ratio is dominant in the identification of vital nodes,but the effect can be notably enhanced by additional node degree ***,interestingly,in Lancichinetti-Fortunato-Radicchi(LFR)synthesis networks,the cycle-dominant network is observed.
Time series anomaly detection is an important task in many applications,and deep learning based time series anomaly detection has made great ***,due to complex device interactions,time series exhibit diverse abnormal ...
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Time series anomaly detection is an important task in many applications,and deep learning based time series anomaly detection has made great ***,due to complex device interactions,time series exhibit diverse abnormal signal shapes,subtle anomalies,and imbalanced abnormal instances,which make anomaly detection in time series still a *** and analysis of multivariate time series can help uncover their intrinsic spatio-temporal characteristics,and contribute to the discovery of complex and subtle *** this paper,we propose a novel approach named Multi-scale Convolution Fusion and Memory-augmented Adversarial AutoEncoder(MCFMAAE)for multivariate time series anomaly *** is an encoder-decoder-based framework with four main ***-scale convolution fusion module fuses multi-sensor signals and captures various scales of temporal ***-attention-based encoder adopts the multi-head attention mechanism for sequence modeling to capture global context *** module is introduced to explore the internal structure of normal samples,capturing it into the latent space,and thus remembering the typical ***,the decoder is used to reconstruct the signals,and then a process is coming to calculate the anomaly ***,an additional discriminator is added to the model,which enhances the representation ability of autoencoder and avoids *** on public datasets demonstrate that MCFMAAE improves the performance compared to other state-of-the-art methods,which provides an effective solution for multivariate time series anomaly detection.
The application of the electronic control unit (ECU) motivates dynamic models with high precision to simulate mechatronic systems for various analysis and design tasks like hardware-in-the-loop (HiL) simulation. Unlik...
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Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both enti...
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Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.
Dialogue-based relation extraction(DialogRE) aims to predict relationships between two entities in dialogue. Current approaches to dialogue relationship extraction grapple with long-distance entity relationships in di...
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Dialogue-based relation extraction(DialogRE) aims to predict relationships between two entities in dialogue. Current approaches to dialogue relationship extraction grapple with long-distance entity relationships in dialogue data as well as complex entity relationships, such as a single entity with multiple types of connections. To address these issues, this paper presents a novel approach for dialogue relationship extraction termed the hypergraphs and heterogeneous graphs model(HG2G). This model introduces a two-tiered structure, comprising dialogue hypergraphs and dialogue heterogeneous graphs, to address the shortcomings of existing methods. The dialogue hypergraph establishes connections between similar nodes using hyper-edges and utilizes hypergraph convolution to capture multi-level features. Simultaneously, the dialogue heterogeneous graph connects nodes and edges of different types, employing heterogeneous graph convolution to aggregate cross-sentence information. Ultimately, the integrated nodes from both graphs capture the semantic nuances inherent in dialogue. Experimental results on the DialogRE dataset demonstrate that the HG2G model outperforms existing state-of-the-art methods.
Air quality assessment plays a crucial role in environmental governance and public health decision making. Traditional assessment methods have limitations in handling multi source heterogeneous data and complex nonlin...
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This article presents a novel adaptive control methodology for achieving robust and accurate tracking control of uncertain nonlinear systems using a combination of barrier functions, global sliding mode control, propo...
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This article presents a novel adaptive control methodology for achieving robust and accurate tracking control of uncertain nonlinear systems using a combination of barrier functions, global sliding mode control, proportional-integral-derivative (PID) controllers, and finite time control techniques. The proposed adaptive barrier-function global PID-type control method is designed to handle both matched and unmatched uncertainties and adjust the control parameters in real time to account for changes in system dynamics and perturbations. It efficiently handles both matched and mismatched uncertainties, ensuring precise tracking performance even amid uncertain dynamics and disturbances. The methodology dynamically adjusts control parameters in real time to accommodate changes in system dynamics, enhancing adaptability and performance. The globality of the suggested controller ensures the absence of a reaching phase and establishes the presence of the sliding mode around the surface right from the beginning. The proposed method has also been expanded to address uncertain dynamic systems with both matched and unmatched disturbances, while accounting for actuator faults and input saturation. The efficacy of the proposed methodology is demonstrated through simulation studies and experimental results on a rotary inverted pendulum (RIP) system, showcasing rapid convergence and exceptional tracking capabilities in practical scenarios. The contributions of this research lie in presenting a novel methodology that significantly contributes to the field of nonlinear control systems, offering a robust framework capable of addressing uncertainties in complex nonlinear systems. The results show that the method achieves fast convergence and excellent tracking performance in the presence of uncertainties and disturbances. The proposed adaptive control methodology stands as a promising approach for overcoming the complexities involved in controlling uncertain nonlinear systems, paving t
Microbial fouling is an important challenge in water recovery system of manned spacecrafts for longer term *** fouling of 5A06 aluminium alloy induced by typical extreme environment-resistant bacteria in oligotrophic ...
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Microbial fouling is an important challenge in water recovery system of manned spacecrafts for longer term *** fouling of 5A06 aluminium alloy induced by typical extreme environment-resistant bacteria in oligotrophic solutions of simulated condensate of manned spacecraft was *** cereus showed poor survival ability to oligotrophic environments,and a small amount of remaining live *** cells mainly existed in the form of spores without forming *** when *** was mixed cultured with Cupriavidus metallidurans,the system was mainly affected by *** biofilms rather than *** *** could promote the thickness of passive films of aluminum alloy,so *** posed a minor threat to the corrosion of 5A06 aluminum ***,*** showed strong adaptability to oligotrophic environments and formed a large number of *** the contamination threat of *** still dominated even cultured with *** when cultured with ***,the threat of contamination from *** still ***,*** would pose a threat of microbial fouling to the oligotrophic water recovery system of manned spacecrafts.
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